目标检测算法——YOLOv5/YOLOv7改进之结合SIoU

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论文题目：SIoU Loss: More Powerful Learning for Bounding Box Regression

论文地址：http://SIoU Loss: More Powerful Learning for Bounding Box Regression

简要介绍：

本文提出了一种新的损失函数SIoU，其中考虑到所需回归之间的向量角度，重新定义了惩罚指标。应用于传统的神经网络和数据集，表明SIoU提高了训练的速度和推理的准确性。SIoU进一步考虑了真实框和预测框之间的向量角度，重新定义相关损失函数，具体包含四个部分：角度损失(Angle cost)、距离损失(Distance cost)、形状损失(Shape cost)、IoU损失（IoU cost）。

1.角度损失(Angle cost)

 如果 ≤/4，收敛过程将首先最小化 ， 否则最小化β：

 并且引入和定义了LF组件：

Angle cost的曲线如图所示：

2. 距离损失(Distance cost)

考虑到上面定义的 Angle cost，重新定义了Distance cost：

  3.形状损失(Shape cost)

Shape cost的定义为： 

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4. IoU损失(IoU cost)

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 最终SIoU损失函数定义如下：

回归损失函数为:

 总的损失函数为：

YOLOv5改进之结合SIoU：

下面直接上干货——————

1.配置metric.py文件

打开./utils/metrics.py文件，并将bbox_iou()整个替换。

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, SIoU=False, eps=1e-7):
    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
    box2 = box2.T
 
    # Get the coordinates of bounding boxes
    if x1y1x2y2:  # x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
    else:  # transform from xywh to xyxy
        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
 
    # Intersection area
    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
 
    # Union Area
    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
    union = w1 * h1 + w2 * h2 - inter + eps
 
    iou = inter / union
    if GIoU or DIoU or CIoU or SIoU:
        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
        if SIoU:    # SIoU Loss https://arxiv.org/pdf/2205.12740.pdf
            s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5
            s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5
            sigma = torch.pow(s_cw ** 2 + s_ch ** 2, 0.5)
            sin_alpha_1 = torch.abs(s_cw) / sigma
            sin_alpha_2 = torch.abs(s_ch) / sigma
            threshold = pow(2, 0.5) / 2
            sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1)
            # angle_cost = 1 - 2 * torch.pow( torch.sin(torch.arcsin(sin_alpha) - np.pi/4), 2)
            angle_cost = torch.cos(torch.arcsin(sin_alpha) * 2 - np.pi / 2)
            rho_x = (s_cw / cw) ** 2
            rho_y = (s_ch / ch) ** 2
            gamma = angle_cost - 2
            distance_cost = 2 - torch.exp(gamma * rho_x) - torch.exp(gamma * rho_y)
            omiga_w = torch.abs(w1 - w2) / torch.max(w1, w2)
            omiga_h = torch.abs(h1 - h2) / torch.max(h1, h2)
            shape_cost = torch.pow(1 - torch.exp(-1 * omiga_w), 4) + torch.pow(1 - torch.exp(-1 * omiga_h), 4)
            return iou - 0.5 * (distance_cost + shape_cost)
        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
            if DIoU:
                return iou - rho2 / c2  # DIoU
            elif CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
                with torch.no_grad():
                    alpha = v / (v - iou + (1 + eps))
                return iou - (rho2 / c2 + v * alpha)  # CIoU
        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
            c_area = cw * ch + eps  # convex area
            return iou - (c_area - union) / c_area  # GIoU
    else:
        return iou  # IoU

2.配置loss.py文件

打开./utils/loss.py文件，找到ComputeLoss类中的__call__()函数，把Regression loss中计算iou的代码做以下修改即可。

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关于YOLOv5的其他改进方法可关注并私信博主的CSDN